Biology subjects

Ying, B.-W.

Publications and source records attributed to Ying, B.-W..

7 recordsLinked to original sources

bioRxiv2026

Cultivation and Sequencing Reveal Nutrient-Dependent Bacterial Responses in Public Restrooms

Microbial communities in indoor environments are shaped by resource availability and disturbances, yet their growth dynamics and compositional changes remain unclear. Here we combined quantitative colony growth analysis with 16S rRNA gene sequencing to investigate bacterial communities on public restroom surfaces before and after routine cleaning under varied nutrient conditions. Cultivation revealed that nutrient availability strongly influenced bacterial growth and selectively enriched distinct taxa, while cleaning caused limited shifts in overall community structure and diversity. Correlations between growth parameters and diversity indices were weak, indicating that taxon-specific responses to nutrients primarily drive growth outcomes. These findings suggest that resource composition, rather than cleaning disturbance, governs bacterial growth and community assembly in built environments. Integrating culture-based phenotyping with sequencing provides a comprehensive framework to understand microbial dynamics following environmental perturbations.

microbiology↗

bioRxiv2026

Equivalent fitness increase achieved by active learning-navigated habitat reconstruction and evolution-induced genome mutation

Fitness increase, impacted by genetic and environmental traits, requires evolutionary changes in genomes or ecological changes in habitats. Whether and how habitat reconstruction can compensate for the genetic changes remains unclear. The present study offers an experimental comparison of bacterial fitness increase via evolutionary and ecological strategies to verify that habitat reconstruction has the potential to avert genetic restriction, challenging the genetic-only view of adaptation. Six finely tuned media with different combinations and five evolved lineages with different mutations, both of which allowed equivalent increases in bacterial growth rates, were achieved through active machine learning and experimental evolution, respectively. Transcriptome changes accompanied by fitness increase were limited but divergent among the six fitted media, compared to changes that were broad but similar among the five fitted genomes. The universal transcriptome reorganization in fitted genomes and media was likely driven by surrounding metabolisms, indicating escape routes for genetic changes that increase fitness through habitat reconstruction.

systems biology↗

bioRxiv2023

Diversity for commonality in the evolutionary changes of the reduced genome to regain the growth fitness

As the genome encodes the information crucial for cell growth, a sizeable genomic deficiency often causes a significant decrease in growth fitness. Whether and how the decreased growth fitness caused by genome reduction could be compensated by evolution was investigated here. Experimental evolution with an Escherichia coli strain carrying a reduced genome was conducted in multiple lineages for approximately 1,000 generations. The growth rate, which was largely declined due to genome reduction, was considerably recovered, associated with the improved carrying capacity. Genome mutations accumulated during evolution were significantly varied across the evolutionary lineages and were randomly localized on the reduced genome. Transcriptome reorganization showed a common evolutionary direction and conserved the chromosomal periodicity, regardless of highly diversified gene categories, regulons, and pathways enriched in the differentially expressed genes. Genome mutations and transcriptome reorganization caused by evolution, which were found to be dissimilar to those caused by genome reduction, must have followed divergent mechanisms in individual evolutionary lineages. Gene network reconstruction successfully identified three gene modules functionally differentiated, which were responsible for the evolutionary changes of the reduced genome in growth fitness, genome mutation, and gene expression, respectively. The diversity in evolutionary approaches improved the growth fitness associated with the homeostatic transcriptome architecture as if the evolutionary compensation for genome reduction was like all roads leading to Rome.

evolutionary biology↗

bioRxiv2022

Employing active learning in the optimization of culture medium for mammalian cells

Medium optimization is a crucial step of cell culture for biopharmaceutics and regeneration medicine. It remains challenging, as both media and cells are highly complex systems. To address the issue, we tried active learning to fine-tune the culture medium by combining the high-throughput assay and machine learning. As a pilot study, the cell line HeLa-S3 and the gradient-boosting decision tree algorithm were used. The regular and time-saving approaches were developed, and both successfully fine-tuned 29 components to achieve improved cell culture than the original medium. The fine-tuned media showed a significant decrease in fetal bovine serum and the differentiation in vitamins and amino acids. Unexpectedly, the medium optimization raised the cellular NAD(P)H abundance but not the cell concentration owing to the conventional method used for cell culture assay. Our study demonstrated the efficiency of active learning for medium optimization and provided valuable hints for employing machine learning in cell culture.

systems biology↗

bioRxiv2022

Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity

Microorganisms growing in their habitat constitute a complex system. How the individual constituents of the environment contribute to microbial growth remains largely unknown. The present study focused on the contribution of environmental constituents to population dynamics via a high-throughput assay and data-driven analysis of a wild-type Escherichia coli strain. A large dataset constituting a total of 12,828 bacterial growth curves with 966 medium combinations, which were composed of 44 pure chemical compounds, was acquired. Machine learning analysis of the big data relating the growth parameters to the medium combinations revealed that the decision-making components for bacterial growth were distinct among various growth phases, e.g., glucose, sulfate and serine for maximum growth, growth rate and growth delay, respectively. Further analyses and simulations indicated that branched-chain amino acids functioned as global coordinators for population dynamics, as well as, a survival strategy of risk diversification to prevent the bacterial population from undergoing extinction.

systems biology↗

bioRxiv2020

The common chromosomal periodicity of transcriptomes is correlated with the bacterial growth rate

The growth rate, representing the fitness of a bacterial population, is determined by the whole transcriptome. Chromosomal periodicity is a representative overall feature of the whole transcriptome; however, whether and how it is associated with the bacterial growth rate are unknown. To address these questions, we analyzed a total of 213 transcriptomes of genetically differentiated Escherichia coli strains growing in an assortment of culture conditions varying in terms of temperature, nutrition level and osmotic pressure. Intriguingly, the Fourier transform identified a common chromosomal periodicity of transcriptomes, which was independent of the variation in genomes and environments. In addition, fitting of the theoretical model found that the amplitudes of the periodic transcriptomes were significantly correlated with the growth rates. This novel finding successfully identified a single parameter representing the global pattern of the whole transcriptome for the first time and indicated that bacterial growth was correlated with the magnitude of chromosomal differentiation in gene expression. These results provided an alternative global parameter for evaluating the adaptiveness of a growing bacterial population and provided a quantitative rule that makes it possible to predict the growth dynamics according to the gene expression pattern.

genomics↗

bioRxiv2018

A decay effect of bacterial growth rate associated with genome reduction

Bacterial growth is an important topic in microbiology and of crucial importance to better understand living cells. Bacterial growth dynamics are quantitatively examined using various methods to determine the physical, chemical or biological features of growing populations. Due to methodological differences, the exponential growth rate, which is a parameter that is representative of growth dynamics, should be differentiated. This study experimentally verified the differentiation in growth rates attributed to different methodologies, and demonstrated that the most popular method, optical turbidity, led to the determination of a lower growth rate in comparison to the methods based on colony formation and ATP abundance, due to a decay effect of reading OD600 during a population increase. Accordingly, the logistic model, which is often applied to growth data reading the OD600, was revised by introducing a new parameter: the decay rate, to compensate for the lowered estimation in growth rates. The modified logistic model not only presented an improved goodness of fit in comparison to the original model but also led to an intriguing finding of a correlation between genome reduction and the decay rate. The decay effect seemed to be partially attributed to the decrease in cell size accompanied by a population increase and was medium dependent. In summary, the present study provides not only a better theoretical tool for the high-throughput studies on bacterial growth dynamics linking with experimental data using optical turbidity to the theoretical analysis with biological importance, but also a valuable insight for understanding the genome evolution and fitness increase in microbial life.

microbiology↗